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Record W3175097559 · doi:10.2196/29671

Mental Health Service User and Worker Experiences of Psychosocial Support Via Telehealth Through the COVID-19 Pandemic: Qualitative Study

2021· article· en· W3175097559 on OpenAlexvenueno aff
Annie Venville, Sarah O’Connor, Hannah Roeschlein, Priscilla Ennals, Grace McLoughlan, Neil Thomas

Bibliographic record

VenueJMIR Mental Health · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthPsychosocialMental healthVideoconferencingTelemedicineNursingMedicineService (business)DistressQualitative researchPsychologyPandemicHealth carePsychiatryCoronavirus disease 2019 (COVID-19)MultimediaClinical psychologyBusinessSociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: During the COVID-19 pandemic, we saw telehealth rapidly become the primary way to receive mental health care. International research has validated many of the benefits and challenges of telehealth known beforehand for specific population groups. However, if telehealth is to assume prominence in future mental health service delivery, greater understanding of its capacity to be used to provide psychosocial support to people with complex and enduring mental health conditions is needed. OBJECTIVE: We focused on an Australian community-managed provider of psychosocial intervention and support. We aimed to understand service user and worker experiences of psychosocial support via telehealth throughout the COVID-19 pandemic. METHODS: This study was jointly developed and conducted by people with lived experience of mental ill health or distress, mental health service providers, and university-based researchers. Semistructured interviews were conducted between August and November 2020 and explored participant experiences of receiving or providing psychosocial support via telehealth, including telephone, text, and videoconferencing. Qualitative data were analyzed thematically; quantitative data were collated and analyzed using descriptive statistics. RESULTS: Service users (n=20) and workers (n=8) completed individual interviews via telephone or videoconferencing platform. Service users received psychosocial support services by telephone (12/20, 60%), by videoconferencing (6/20, 30%), and by both telephone and videoconferencing (2/20, 10%). Of note, 55% (11/20) of service user participants stated a future preference for in-person psychosocial support services, 30% (6/20) preferred to receive a mixture of in-person and telehealth, and 15% (3/20) elected telehealth only. Two meta-themes emerged as integral to worker and service user experience of telehealth during the pandemic: (1) creating safety and comfort and (2) a whole new way of working. The first meta-theme comprises subthemes relating to a sense of safety and comfort while using telehealth; including trusting in the relationship and having and exercising choice and control. The second meta-theme contains subthemes reflecting key challenges and opportunities associated with the shift from in-person psychosocial support to telehealth. CONCLUSIONS: Overall, our findings highlighted that most service users experienced telehealth positively, but this was dependent on them continuing to get the support they needed in a way that was safe and comfortable. While access difficulties of a subgroup of service users should not be ignored, most service users and workers were able to adapt to telehealth by focusing on maintaining the relationship and using choice and flexibility to maintain service delivery. Although most research participants expressed a preference for a return to in-person psychosocial support or hybrid in-person and telehealth models, there was a general recognition that intentional use of telehealth could contribute to flexible and responsive service delivery. Challenges to telehealth provision of psychosocial support identified in this study are yet to be fully understood.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.512
Teacher spread0.409 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2021
Admission routes1
Has abstractyes

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